Mass adoption of the Internet by both consumers and businesses has resulted in the use of the Internet for product advertising and research. For example, a manufacturer or a retailer may rely on its on-line site to inform consumers of the products it has for sale. Every day upwards of 20 million people research products on-line before buying them off-line. A consumer may visit a product manufacturer's Web site to evaluate features and functions of different products and possibly compare them to products offered by competing manufacturers.
Even with the advent of on-line research, it has been observed that many consumers still limit their use of the Internet for product research and visit “brick and mortar” stores to make their product purchases. An important factor that consumers consider in deciding whether to act on this product research and purchase a particular product is the availability of an item for sale at a brick and mortar location near them. The consumers who research online and then buy off-line represent upwards of $1 trillion in purchasing power and are more likely to buy in stores, which are likely to have the item in stock. Given that consumers can actually develop a focused, well-informed product preference before ever visiting a store, if a retailer's brick and mortar store is unlikely to have an advertised product in stock, a consumer would probably not choose to go to that particular store to purchase the desired product.
As such, consumers have an interest to know whether a store will have an item in stock at the time they wish to purchase a product. However, there is no retailing structure around this behavioral pattern beyond that of a consumer telephoning or actually visiting individual stores that he knows of in the area to determine if those stores have a desired product in stock. In addition, if a store will not set aside a product for a consumer, there is no way for a consumer to predict whether the product will still be available for purchase when the consumer actually visits the store.
Currently, retailers utilize batch processing of sales data to forecast their need to replenish product inventories. Retailers may have multiple brick and mortar stores that each may have multiple cash registers. At end of the day, the sales data from all cash registers of a particular store are transferred to an inventory computer that compiles and stores all the data. The sales data from each store's inventory computer is then uploaded to a corporate inventory server that collects the sales data from all of a retailer's stores. As such, when a purchase is made early in the morning, a retailer's corporate inventory server may not obtain the sales data for that purchase until much after the close of business. Although, some retailers are slowly migrating to systems where individual stores are uploading sales data soon after a purchase transaction is conducted, neither of these types of inventory processing systems provide product availability information to consumers, much less on an industry-wide basis.
In addition, customers may choose to visit the retail store some time after researching the product on line. During this time period, other customers will arrive randomly at the store and some may purchase the same item, thus reducing the retailer's inventory. A customer who checks the current on line availability has no means of forecasting the likelihood that the desired product will be purchased by another customer during this time period.
Finally, retail inventory records are often highly inaccurate. A recent empirical study conducted by faculty at the Harvard Business School found that only approximately ⅓ of the retail store inventory data records were actually correct. (Ananth Raman and Nicole deHoratius, “Inventory Record Inacccuracy: An Empirical Study,” Harvard Business School.) That is, theft, damage and misplaced items create “unreported demands” that reduce the retailer's inventory below what is indicated by the inventory records. Potential customers have no information regarding this unreported demand and no way to account for it in their decision making.